#!/usr/bin/env python
# -*- coding:utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
from collections import OrderedDict
import torch.nn as nn

from lib.models.tools.module_helper import ModuleHelper

__all__ = [
    "ResNet",
    "resnet18",
    "resnet34",
    "resnet50",
    "resnet101",
    "resnet152",
    "resnext50_32x4d",
    "resnext101_32x8d",
]


model_urls = {
    "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
    "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
    "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
    "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
    "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
    "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
    "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation,
    )


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        bn_type=None,
    ):
        super(BasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes)
        self.relu = nn.ReLU(inplace=False)
        self.relu_in = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out = out + identity
        out = self.relu_in(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        bn_type=None,
    ):
        super(Bottleneck, self).__init__()
        width = int(planes * (base_width / 64.0)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * self.expansion)
        self.relu = nn.ReLU(inplace=False)
        self.relu_in = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out = out + identity
        out = self.relu_in(out)
        return out


class ResNet(nn.Module):
    def __init__(
        self,
        block,
        layers,
        num_classes=1000,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        bn_type=None,
    ):
        super(ResNet, self).__init__()

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group

        self.resinit = nn.Sequential(
            OrderedDict(
                [
                    (
                        "conv1",
                        nn.Conv2d(
                            3,
                            self.inplanes,
                            kernel_size=7,
                            stride=2,
                            padding=3,
                            bias=False,
                        ),
                    ),
                    ("bn1", ModuleHelper.BatchNorm2d(bn_type=bn_type)(self.inplanes)),
                    ("relu1", nn.ReLU(inplace=False)),
                ]
            )
        )

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], bn_type=bn_type)
        self.layer2 = self._make_layer(
            block,
            128,
            layers[1],
            stride=2,
            dilate=replace_stride_with_dilation[0],
            bn_type=bn_type,
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1],
            bn_type=bn_type,
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=2,
            dilate=replace_stride_with_dilation[2],
            bn_type=bn_type,
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False, bn_type=None):
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
                self.groups,
                self.base_width,
                previous_dilation,
                bn_type=bn_type,
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    bn_type=bn_type,
                )
            )

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.resinit(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)

        return x


def ResNext(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
        model.load_state_dict(state_dict)
    return model


class ResNextModels(object):
    def __init__(self, configer):
        self.configer = configer

    def resnext101_32x8d(self, **kwargs):
        """Constructs a ResNeXt-101 32x8d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs["groups"] = 32
        kwargs["width_per_group"] = 8
        model = ResNext(
            "resnext101_32x8d",
            Bottleneck,
            [3, 4, 23, 3],
            pretrained,
            progress,
            bn_type=self.configer.get("network", "bn_type"),
            **kwargs
        )
        model = ModuleHelper.load_model(
            model,
            pretrained=self.configer.get("network", "pretrained"),
            all_match=False,
            network="resnext",
        )
        return model

    def resnext101_32x16d(self, **kwargs):
        """Constructs a ResNeXt-101 32x16d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs["groups"] = 32
        kwargs["width_per_group"] = 16
        model = ResNext(
            "resnext101_32x16d",
            Bottleneck,
            [3, 4, 23, 3],
            pretrained,
            progress,
            bn_type=self.configer.get("network", "bn_type"),
            **kwargs
        )
        model = ModuleHelper.load_model(
            model,
            pretrained=self.configer.get("network", "pretrained"),
            all_match=False,
            network="resnext",
        )
        return model

    def resnext101_32x32d(self, **kwargs):
        """Constructs a ResNeXt-101 32x32d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs["groups"] = 32
        kwargs["width_per_group"] = 32
        model = ResNext(
            "resnext101_32x32d",
            Bottleneck,
            [3, 4, 23, 3],
            pretrained,
            progress,
            bn_type=self.configer.get("network", "bn_type"),
            **kwargs
        )
        model = ModuleHelper.load_model(
            model,
            pretrained=self.configer.get("network", "pretrained"),
            all_match=False,
            network="resnext",
        )
        return model

    def resnext101_32x48d(self, **kwargs):
        """Constructs a ResNeXt-101 32x48d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs["groups"] = 32
        kwargs["width_per_group"] = 48
        model = ResNext(
            "resnext101_32x48d",
            Bottleneck,
            [3, 4, 23, 3],
            pretrained,
            progress,
            bn_type=self.configer.get("network", "bn_type"),
            **kwargs
        )
        model = ModuleHelper.load_model(
            model,
            pretrained=self.configer.get("network", "pretrained"),
            all_match=False,
            network="resnext",
        )
        return model
